Purpose -The purpose of this study is to analyse the validity of the value added intellectual coefficient (VAIC) method as an indicator of intellectual capital. Design/methodology/approach -The paper describes VAIC through its calculation formulae and aims to establish what exactly it is that the method measures. It also looks in detail at how intellectual capital is understood in the method, and discusses its conceptual confusions. Furthermore, the paper tests the hypothesis according to which VAIC correlates with a company's stock market value, and reflects the contradictory results of earlier studies. Findings -The analyses show, first, that VAIC indicates the efficiency of the company's labour and capital investments, and has nothing to do with intellectual capital. Furthermore, the calculation method uses overlapping variables and has other serious validity problems. Second, the results do not lend support to the hypothesis that VAIC correlates with a company's stock market value. The main reasons behind the lack of consistency in earlier VAIC results lie in the confusion of capitalized and cash flow entities in the calculation of structural capital and in the misuse of intellectual capital concepts. Practical implications -The analyses show that VAIC is an invalid measure of intellectual capital. Originality/value -The result is important since the method has been widely used in micro and macro level analyses, but this is the first time it has been put to rigorous scientific analysis.
PurposeThis paper seeks to examine the calculated intangible value (CIV) method as a measure of intellectual capital (intangibles) in enterprises. The aim is to show the benefits and disadvantages of the method and its actual relation to intellectual capital.Design/methodology/approachThe authors present a conceptual, theoretical and empirical analysis of CIV to assess its validity as a measure of firm's intangibles.FindingsThe result of the analyses is that CIV is connected to all types of capital assets (physical, financial, combined physical and financial and intangible) and thus it does not unambiguously relate or measure firm's intangible value(s). CIV should be seen solely as a measure of financial efficiency derived from companies' return on assets (ROA). CIV measures an overall financial recognized comparative advantage in comparison with competitors within the same branch of industry. There is no evidence to support the simplistic assumption that a company's CIV is a measure of its intellectual capital.Originality/valueCIV is used quite widely for purposes of measuring intellectual capital in companies and industries. However, its validity has never been subjected to critical examination. The results of this paper provide valuable information on the reliable measurement of intellectual capital and the further development of these measurements.
Research purpose. The study is focused on the Covid-19 pandemic crisis in the European Union. This study investigates the current driving trends and trade-offs of the Covid-19 pandemic phenomenon and social inclusion trends in the European countries. Design / Methodology / Approach. The methodology is based on conventional statistical index theory and statistics. The study investigates cases, deaths, and key Covid-19 statistics. The research design combines key social inclusion statistics of the Eurostat and the official Covid-19 statistics of the European Centre for Disease Prevention and Control. Covid-19 data is updated to 1.3.2021. Social inclusion variables are selected from the Eurostat database. Social inclusion variables cover poverty, material deprivation, income distribution, income, quality of life, employment, and education matters. Scattering matrices on the relationships among the key variables under review are reported. Findings. The study reports basic trends of Covid-19 cases, deaths, deaths/cases and calculates these Covid-19 trends in 29 European countries. This study reports trade-off analyses of key social inclusion trends of the European Union countries. Key indicators are linked to economic income, income distribution, poverty, gender issues, and housing statistics. The 19 key indicators of social inclusion are analysed and reported with Covid-19 data. Statistical correlation analysis tables (2a and 2b) are calculated with key European social inclusion indicators. The study reveals some relevant aspects of the social inclusion policy of the European Union about the ongoing Covid-19 crisis and exit strategies. Originality / Value / Practical implications. This conference paper demonstrates novel and exciting possibilities of integrated data pooling (The Eurostat and the European Centre for Disease Prevention and Control). Original results of key trend drivers are provided by the authors. Value-adding and interesting results are delivered for European governments and the business community. Results and findings of the study can be used in the planning of economic recovery and Covid-19 exit policies in the member states of the European Union.
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